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Advanced financial risk forecasting using enhanced kernel-based extreme learning machines: Tackling challenges in bankruptcy problem

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Ain Shams engineering Journal 2025年第9期16卷

作者： Songyun Ye Mohammad Khishe Banar Fareed Ibrahim Aseel Smerat School of Computer Science and Engineering Guangzhou Institute of Science and Technology Guangzhou China Centre for Research Impact & Outcome Chitkara University Institute of Engineering and Technology Chitkara University Rajpura 140401 Punjab India Department of Biosciences Saveetha School of Engineering. Saveetha Institute of Medical and Technical Sciences Chennai 602 105 India Department of Electrical Engineering Imam Khomeini Naval Science University of Nowshahr Nowshahr Iran Applied Science Research Center Applied Science Private University Amman Jordan Department of Information Technology Lebanese French University Kurdistan Region Iraq Faculty of Educational Sciences Al-Ahliyya Amman University Amman 19328 Jordan Technologies Engineering Mazaya University College Nasiriyah Iraq

Accurately forecasting financial risk, particularly in bankruptcy forecasting, remains a significant challenge due to the non-linear and complex nature of economic indicators. Conventional statistical techniques often fail to capture the intricate relationships within financial datasets. This research addresses these shortcomings using a Kernel-based Extreme Learning Machine (KELM). KELM stands out for its capability to discern complex patterns, yet the model’s full potential is often underutilized due to the intricate hyperparameter tuning process. We identify a critical research gap: the need for sophisticated algorithms that can effectively fine-tune KELM tailored for financial risk forecasting. To bridge this gap, our study develops the Objective-based Survival Individual Enhancement Chimp Optimization Algorithm (OBSIECOA). This innovative algorithm elevates hyperparameter optimization by bolstering weaker predictions and preserving diversity among the strongest. It strategically reallocates the elite candidates during iterative cycles, enabling an extensive and dynamic exploration of the hyperparameter space. Our methodology emphasizes the importance of precision in financial forecasting models, and the OBSIECOA’s contribution is quantitatively assessed through rigorous evaluation against several KELM variants: standard KELM, KELM-DBOA, KELM-MOA, KELM-NFVOA, and KELM-WOBCOA. The assessment uses two real-world financial datasets, the Japanese Dataset (JPNBDS) and the Wieslaw dataset, with performance metrics including RMSE, NSEF, and bias. The KELM-OBSIECOA framework significantly improves forecasting accuracy over traditional models, confirming its efficacy as a superior early warning system for financial distress. Our findings underscore the necessity for and effectiveness of KELM approaches in overcoming the inherent challenges of financial risk prediction. The research advocates for integrating such advanced models in financial analysis, highlighting their s

关键词： Risk forecasting Enhanced kernel Extreme learning machines Bankruptcy

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Post-2000 Nonlinear Optical Materials and Measurements: Data Tables and Best Practices

arXiv

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arXiv 2023年

作者： Vermeulen, Nathalie Espinosa, Daniel Ball, Adam Ballato, John Boucaud, Philippe Boudebs, Georges Campos, Cecília L.A.V. Dragic, Peter Gomes, Anderson S.L. Huttunen, Mikko J. Kinsey, Nathaniel Mildren, Rich Neshev, Dragomir Padilha, Lázaro A. Pu, Minhao Secondo, Ray Tokunaga, Eiji Turchinovich, Dmitry Yan, Jingshi Yvind, Kresten Dolgaleva, Ksenia Van Stryland, Eric W. Brussels Photonics B-PHOT Department of Applied Physics and Photonics IR-TONA Vrije Universiteit Brussel Pleinlaan 2 Brussel1050 Belgium School of Electrical Engineering and Computer Science University of Ottawa OttawaONK1N 6N5 Canada Dept. Of Electrical & Computer Engineering Virginia Commonwealth University RichmondVA23284 United States Dept of Materials Science and Engineering Clemson University ClemsonSC29634 United States Université Côte d’Azur CNRS CRHEA Rue Bernard Grégory Sophia-Antipolis06905 France Univ Angers LPHIA SFR MATRIX AngersF-49000 France Departamento de Física Universidade Federal de Pernambuco PE Recife50670-901 Brazil Dept. Of Electrical & Computer Engineering University of Illinois at Urbana-Champaign UrbanaIL61801 United States Tampere University Photonics Laboratory Physics Unit TampereFI-33014 Finland MQ Photonics Research Centre Macquarie University NSW2109 Australia ARC Centre of Excellence for Transformative Meta-Optical Systems TMOS Department of Electronic Materials Engineering Research School of Physics Australian National University CanberraACT2601 Australia Instituto de Fisica "Gleb Wataghin" Universidade Estadual de Campinas Campinas Sao Paulo13083-970 Brazil Department of Electrical and Photonics engineering Technical University of Denmark Kgs. LyngbyDK-2800 Denmark Department of Physics Faculty of Science Tokyo University of Science 1-3 Kagurazaka Shinjuku-ku Tokyo162-8601 Japan Fakultät für Physik Universität Bielefeld Universitätsstr. 25 Bielefeld33615 Germany CREOL The College of Optics and Photonics University of Central Florida OrlandoFL United States

In its 60 years of existence, the field of nonlinear optics has gained momentum especially over the past two decades thanks to major breakthroughs in material science and technology. In this article, we present a new set of data tables listing nonlinear-optical properties for different material categories as reported in the literature since 2000. The papers included in the data tables are representative experimental works on bulk materials, solvents, 0D-1D-2D materials, metamaterials, fiber waveguiding materials, on-chip waveguiding materials, hybrid waveguiding systems, and materials suitable for nonlinear optics at THz frequencies. In addition to the data tables, we also provide best practices for performing and reporting nonlinear-optical experiments. These best practices underpin the selection process that was used for including papers in the tables. While the tables indeed show strong advancements in the field over the past two decades, we encourage the nonlinear-optics community to implement the identified best practices in future works. This will allow a more adequate comparison, interpretation and use of the published parameters, and as such further stimulate the overall progress in nonlinear-optical science and applications. © 2023, CC BY.

关键词： Nonlinear optics

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Linearity assessment between lower limb joint angles and angular accelerations at standard maximum vertical jumps with long-short and no countermovement

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Gait & Posture 2023年 106卷 S174-S175页

作者： Rodrigues, Carlos Correia, Miguel Abrantes, João Benedetti, Marco Nadal, Jurandir Faculty of Engineering- University of Porto Biomedical Engineering Program Porto Portugal INESC TEC - Institute for Systems and Computer Engineering- Technology and Science C-BER - Centre for Biomedical Engineering Research Porto Portugal Faculty of Engineering- University of Porto Department of Electrical and Computer Engineering Porto Portugal CICANT - Centre for Research in Applied Communication- Culture and New Technologies MovLab - Laboratory of Technologies for Interactions and Interfaces Lisbon Portugal Federal University of Pernambuco Department of Electronic and Systems Recife Brazil Federal University of Rio de Janeiro Biomedical Engineering Program Rio de Janeiro Brazil

IntroductionStandard maximum vertical jump (MVJ) has been applied to assess lower limb long countermovement (CM) on countermovement jump (CMJ) and short CM on drop jump (DJ) for comparison to squat jump (SJ) without CM [1,2]. Research questionNevertheless, the ability for lower limb joint accelerations depending on lower limb joint angles at long, short and no CM remains an open issue [3,4], in particular linearity assessment of these relations at long, short and no CM in association with MVJ whole-body impulsion. MethodsFor this reason, we applied piecewise linear detection of segmented subphases on lower limb joint angles ( θ)-angular acceleration ( α) diagrams for the hip (H), knee (K) and ankle (A) during impulse phases on CMJ, DJ and SJ. Selected sample corresponds to best MVJ trials based on higher flight-time of 3 CMJ, DJ and SJ repetitions for six healthy untrained subjects with ages (21.5±1.4) years, (76.7±9.3)kg mass and (1.79±0.06)m height. Adhesive reflective markers were applied at lower limb and torso, with sagittal hip, knee, and ankle θ, α obtained by inverse kinematics. H,K,A ( θ, α) diagrams were plotted and impulsion subphases segmented at selected instants of ∂ a/∂ q phase reversal with linear fit by the least-squares method, Fig. 1. Fig. 1. Hip, knee, ankle joint ( θ, α) diagrams at CMJ, DJ, SJ with segmented subphases time span(Δt), |∂α/∂θ| slope magnitudes and R² linear fit regression. ResultsSJ presented larger number of ( θ, α) subphases than DJ, both larger than CMJ with higher |∂ α/∂ θ| variation at DJ and SJ than CMJ, pointing to higher complexity of neuromuscular control and contributing to explain reduced SJ, DJ performance without CM (0.33±0.05)m and with short CM (0.27±0.03)m in relation to CMJ with long CM (0.36±0.04)m for untrained selected subjects, Fig. 1. Thus, despite the larger total time span Δt at CMJ (0.76±0.10)s in relation to SJ (0.36±0.08)s and DJ (0.23±0.05)s, CMJ presented lower number of subphases with longer time span

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Muscle coactivation analysis for neuromuscular control assessment of lower limb stretch-shortening cycle

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Gait & Posture 2023年 106卷 S175-S176页

IntroductionMuscle stretch-shortening cycle (SSC) is a central mechanism with lower limb muscle contraction immediately preceded by muscle stretch for efficient submaximal activities such as gait and powerful maximal activities such as running and jumping [1,2]. Research questionAlthough muscle SSC can be observed at gait and running its higher expression and accessibility is performed on standard maximum vertical jump (MVJ) with an open issue on neuromuscular control assessment of lower limb muscle SSC [3]. MethodsFor this purpose, we present and applied noninvasive subject specific analysis of lower limb muscle coactivation for selected muscles with higher contribution during MVJ impulse based on corresponding conditioned surface electromyographic signals (sEMG). Selected muscles correspond to lower limb muscles vastus medialis (VM), rectus femoris (RF), vastus lateralis (VL), lateral gastrocnemius (LG) and medial gastrocnemius (MG). Twenty-seven trials were assessed corresponding for each subject to the highest MVJ based on larger flight time with long SSC at countermovement jump (CMJ), short SSC at drop jump (DJ) and squat jump (SJ) with no SSC. Trial sample is composed by a group of six young adult volunteers’ students on sports and physical education degree with the ages (21.5 ± 1.4) years, (76.7 ± 9.3) kg mass and (1.79 ± 0.06) m height. Surface skin was prepared and Aqua-Wet gel Skintact F55 electrodes were applied at bipolar configuration as indicated by SENIAN. VM, RF, VL, LG and MG sEMG linear envelopes were paired plotted with the area under the curves computed as well as the coactivation pairs defined as the common area under the curves normalized to the sum of the corresponding pair individual areas under the curves. Coactivations were compared at CMJ, DJ and SJ as well as among CMJ, DJ and SJ, Fig. 1. ResultsStrongest coactivations with decreasing intensity were detected between VM-RF and LG-MG muscles at CMJ and SJ, whereas at DJ the strongest coacti

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A Distributed Multifactorial Particle Swarm Optimization Approach

TechRxiv

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TechRxiv 2021年

作者： Aboud, Ahlem Rokbani, Nizar Mirjalili, Seyedali Qahtani, Abdulrahman M. Almutiry, Omar Dhahri, Habib Alimi, Adel M. University of Sousse ISITCom Sousse4011 Tunisia BP 1173 Sfax3038 Tunisia High Institute of Applied Science and technolgy of Sousse University of Sousse Tunisia Department of Electrical and Electronic Engineering Science Faculty of Engineering and the Built Environment University of Johannesburg South Africa Centre for Artificial Intelligence Research and Optimisation Torrens University Australia Brisbane Australia Yonsei Frontier Lab Yonsei University Korea Republic of Department of Computer Science College of Computers and Information Technology Taif University P.O.Box. 11099 Taif21944 Saudi Arabia College of Applied Computer Science King Saud University Riyadh Saudi Arabia

Multifactorial Optimization (MFO) and Evolutionary Transfer Optimization (ETO) are new optimization challenging paradigms for which the multi-Objective Particle Swarm Optimization system (MOPSO) may be interesting despite limitations. MOPSO has been widely used in static/dynamic multi-objective optimization problems, while its potentials for multi-task optimization are not completely unveiled. This paper proposes a new Distributed Multifactorial Particle Swarm Optimization algorithm (DMFPSO) for multi-task optimization. This new system has a distributed architecture on a set of sub-swarms that are dynamically constructed based on the number of optimization tasks affected by each particle skill factor. DMFPSO is designed to deal with the issues of handling convergence and diversity concepts separately. DMFPSO uses Beta function to provide two optimized profiles with a dynamic switching behaviour. The first profile, Beta-1, is used for the exploration which aims to explore the search space toward potential solutions, while the second Beta-2 function is used for convergence enhancement. This new system is tested on 36 benchmarks provided by the CEC’2021 Evolutionary Transfer Multi-Objective Optimization Competition. Comparatives with the state-of-the-art methods are done using the Inverted General Distance (IGD) and Mean Inverted General Distance (MIGD) metrics. Based on the MSS metric, this proposal has the best results on most tested problems. © 2021, CC BY.

关键词： Parallel algorithms

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Efficient MedSAMs: Segment Anything in Medical Images on Laptop

arXiv

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arXiv 2024年

作者： Ma, Jun Li, Feifei Kim, Sumin Asakereh, Reza Le, Bao-Hiep Nguyen-Vu, Dang-Khoa Pfefferle, Alexander Wei, Muxin Gao, Ruochen Lyu, Donghang Yang, Songxiao Purucker, Lennart Marinov, Zdravko Staring, Marius Lu, Haisheng Dao, Thuy Thanh Ye, Xincheng Li, Zhi Brugnara, Gianluca Vollmuth, Philipp Foltyn-Dumitru, Martha Cho, Jaeyoung Mahmutoglu, Mustafa Ahmed Bendszus, Martin Pflüger, Irada Rastogi, Aditya Ni, Dong Yang, Xin Zhou, Guang-Quan Wang, Kaini Heller, Nicholas Papanikolopoulos, Nikolaos Weight, Christopher Tong, Yubing Udupa, Jayaram K. Patrick, Cahill J. Wang, Yaqi Zhang, Yifan Contijoch, Francisco McVeigh, Elliot Ye, Xin He, Shucheng Haase, Robert Pinetz, Thomas Radbruch, Alexander Krause, Inga Kobler, Erich He, Jian Tang, Yucheng Yang, Haichun Huo, Yuankai Luo, Gongning Kushibar, Kaisar Amankulov, Jandos Toleshbayev, Dias Mukhamejan, Amangeldi Egger, Jan Pepe, Antonio Gsaxner, Christina Luijten, Gijs Fujita, Shohei Kikuchi, Tomohiro Wiestler, Benedikt Kirschke, Jan S. de la Rosa, Ezequiel Bolelli, Federico Lumetti, Luca Grana, Costantino Xie, Kunpeng Wu, Guomin Puladi, Behrus Martín-Isla, Carlos Lekadir, Karim Campello, Victor M. Shao, Wei Brisbane, Wayne Jiang, Hongxu Wei, Hao Yuan, Wu Li, Shuangle Zhou, Yuyin Wang, Bo AI Collaborative Centre University Health Network Department of Laboratory Medicine and Pathobiology University of Toronto Vector Institute Toronto Canada Peter Munk Cardiac Centre University Health Network Toronto Canada Toronto General Hospital Research Institute University Health Network Department of Computer Science University of Toronto University Health Network Vector Institute Toronto Canada University of Science Vietnam National University Ho Chi Minh City Viet Nam Institute of Computer Science University of Freiburg Freiburg Germany School of Medicine and Health Harbin Institute of Technology Harbin China Division of Image Processing Department of Radiology Leiden University Medical Center Leiden Netherlands Department of System and Control Engineering School of Engineering Institute of Science Tokyo Formerly Tokyo Institute of Technology Tokyo Japan Institute for Anthropomatics and Robotics Karlsruhe Institute of Technology Karlsruhe Germany School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu China School of Electrical Engineering and Computer Science University of Queensland Brisbane Australia School of Cyberspace Hangzhou Dianzi University Hangzhou China Division for Computational Radiology and Clinical AI The Department of Neuroradiology University Hospital Bonn Germany Division for Computational Radiology and Clinical AI The Department of Neuroradiology University Hospital Bonn Germany Department of Neuroradiology Heidelberg University Hospital Heidelberg Germany Division for Computational Radiology and Clinical AI Department of Neuroradiology University Hospital Bonn Germany School of Biomedical Engineering Shenzhen University Shenzhen China School of Biological Science and Medical Engineering Southeast University Nanjing China Department of Urology Cleveland Clinic Cleveland United States Department of Computer Science University of Minnesota Minneapolis United St

Promptable segmentation foundation models have emerged as a transformative approach to addressing the diverse needs in medical images, but most existing models require expensive computing, posing a big barrier to their adoption in clinical practice. In this work, we organized the first international competition dedicated to promptable medical image segmentation, featuring a large-scale dataset spanning nine common imaging modalities from over 20 different institutions. The top teams developed lightweight segmentation foundation models and implemented an efficient inference pipeline that substantially reduced computational requirements while maintaining state-of-the-art segmentation accuracy. Moreover, the post-challenge phase advanced the algorithms through the design of performance booster and reproducibility tasks, resulting in improved algorithms and validated reproducibility of the winning solution. Furthermore, the best-performing algorithms have been incorporated into the open-source software with a user-friendly interface to facilitate clinical adoption. The data and code are publicly available to foster the further development of medical image segmentation foundation models and pave the way for impactful real-world applications. © 2024, CC BY-NC-SA.

关键词： Open source software

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The ACROBAT 2022 challenge: Automatic registration of breast cancer tissue

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Medical Image Analysis 2024年 97卷 103257页

作者： Weitz, Philippe Valkonen, Masi Solorzano, Leslie Carr, Circe Kartasalo, Kimmo Boissin, Constance Koivukoski, Sonja Kuusela, Aino Rasic, Dusan Feng, Yanbo Pouplier, Sandra Sinius Sharma, Abhinav Eriksson, Kajsa Ledesma Robertson, Stephanie Marzahl, Christian Gatenbee, Chandler D. Anderson, Alexander R.A. Wodzinski, Marek Jurgas, Artur Marini, Niccolò Atzori, Manfredo Müller, Henning Budelmann, Daniel Weiss, Nick Heldmann, Stefan Lotz, Johannes Wolterink, Jelmer M. De Santi, Bruno Patil, Abhijeet Sethi, Amit Kondo, Satoshi Kasai, Satoshi Hirasawa, Kousuke Farrokh, Mahtab Kumar, Neeraj Greiner, Russell Latonen, Leena Laenkholm, Anne-Vibeke Hartman, Johan Ruusuvuori, Pekka Rantalainen, Mattias Department of Medical Epidemiology and Biostatistics Karolinska Insitutet Stockholm Sweden Institute of Biomedicine University of Turku Turku Finland Institute of Biomedicine University of Eastern Finland Kuopio Finland Department of Surgical Pathology Zealand University Hospital Roskilde Denmark Department of Oncology and Pathology Karolinska Institutet Stockholm Sweden Gestalt Diagnostics Spokane United States Department of Integrated Mathematical Oncology Moffitt Cancer Center Tampa United States Informatics Institute University of Applied Sciences Western Switzerland Switzerland Department of Measurement and Electronics AGH University of Kraków Poland Department of Computer Science University of Geneva Geneva Switzerland Department of Neuroscience University of Padova Italy Medical Faculty University of Geneva Switzerland Fraunhofer Institute for Digital Medicine MEVIS Lübeck Germany Department of Applied Mathematics Technical Medical Centre University of Twente Enschede Netherlands Multimodality Medical Imaging Technical Medical Centre University of Twente Enschede Netherlands Department of Electrical Engineering Indian Institute of Technology Bombay India Graduate School of Engineering Muroran Institute of Technology Hokkaido Japan Faculty of Medical Technology Niigata University of Health and Welfare Niigata Japan FORXAI Business Operations Konica Minolta Inc. Osaka Japan Department of Computing Science University of Alberta EdmontonAlberta United States Alberta Machine Intelligence Institute Edmonton Canada MedTechLabs BioClinicum Karolinska University Hospital Stockholm Sweden Faculty of Medicine and Health Technology Tampere University Tampere Finland

The alignment of tissue between histopathological whole-slide-images (WSI) is crucial for research and clinical applications. Advances in computing, deep learning, and availability of large WSI datasets have revolutionised WSI analysis. Therefore, the current state-of-the-art in WSI registration is unclear. To address this, we conducted the ACROBAT challenge, based on the largest WSI registration dataset to date, including 4,212 WSIs from 1,152 breast cancer patients. The challenge objective was to align WSIs of tissue that was stained with routine diagnostic immunohistochemistry to its H&E-stained counterpart. We compare the performance of eight WSI registration algorithms, including an investigation of the impact of different WSI properties and clinical covariates. We find that conceptually distinct WSI registration methods can lead to highly accurate registration performances and identify covariates that impact performances across methods. These results provide a comparison of the performance of current WSI registration methods and guide researchers in selecting and developing methods. © 2024

关键词： Tissue

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Analysis of the use of fiber-optic sensors in the road traffic

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IFAC-PapersOnLine 2018年第6期51卷 420-425页

作者： Nedoma, J. Fajkus, M. Martinek, R. Vanus, J. Kepak, S. Kahankova, R. Jaros, R. Cvejn, D. Prauzek, Michal Department of Telecommunications Faculty of Electrical Engineering and Computer Science VSB-Technical University of Ostrava Czech Republic Department of Cybernetics and Biomedical Engineering Faculty of Electrical Engineering and Computer Science VSB-Technical University of Ostrava Czech Republic Nanotechnology Centre VSB-Technical University of Ostrava Ostrava Czech Republic

This publication focuses on the use of fiber-optic sensors in the transport applications. Fiberoptic sensors are based on the fiber-optic communications, using nearly identical elements and components as classic fiber-optic communications. The fundamental difference is that in the fiber-optic communications we limit the effects of the environment on the transmission of the information itself, whereas, in the case of fiber-optic sensors, different arrangements are sought to maximize these impacts. In this publication, so-called interferometric sensors are described which work on the basis of comparison of light phases in two parts (the measuring and reference arm). These phase differences can be measured with extreme sensitivity, so fiber-optic interferometers are much more accurate than most other sensors. The base of the structure is an optical fiber, so sensors are immune to electromagnetic interference (EMI) and offer the simplicity of implementation, as the sensors do not require destructive installation on the roads. The publication describes the use of a Mach-Zehnder interferometric sensor type. This type of sensor has been practically validated and discussed for the automotive applications. The primary tracked parameter is the monitoring of the traffic density. The results presented in this publication are based on the evaluation of long-term time series containing hundreds of samples and confirm the idea of using fiber-optic sensors in transport applications. © 2018

关键词： Fiber optic sensors Density (specific gravity) Electromagnetic pulse Fiber optics Interferometers Interferometry Nondestructive examination Optical fibers Publishing Roads and streets Smart city Fiber optic interferometers Mach Zehnder Nondestructive sensors Road traffic Speed measurement

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Thermal and electrical Conductivities for Ester Oil Based Graphene Nana-sheets

Thermal and Electrical Conductivities for Ester Oil Based Gr...

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International Conference on High Voltage engineering and Application, ICHVE

作者： M. M. Emara G. D. Peppas A. D. Polykrati S. Tegopoulos E. C. Pyrgioti D. D. Chronopoulos A. Kyritsis I. F. Gonos School of Electrical and Computer Engineering National Technical University of Athens Athens Greece Electrical and Computer Engineering University of Patras Patra Greece School of Applied Mathematical and Physical Sciences National Technical University of Athens Athens Greece Faculty of Science Regional Centre of Advanced Technologies and Materials Palacky University Olomouc Olomouc Czech Republic

ISBN: (数字)9781728155111

ISBN: (纸本)9781728155128

In this study, nanofluids were prepared using Graphene nano-sheets with two concentrations 0.002% and 0.004%. Thermal conductivity was studied to investigate thermal properties for nanofluid compared to pure oil. Thermal conductivity was calculated using the measured values of thermal diffusivity and specific heat values at range of temperatures of (30°C- 90°C). Thermal diffusivity and specific heat values were measured by LFA (Laser Flash analysis) and DSC (Differential Scanning Calorimetry), respectively. Furthermore, Broadband dielectric spectroscopy was used to measure electrical conductivity for nanofluids for a range of frequencies (0.1Hz to 1MHz) and a range of temperatures for heating and cooling. Temperatures range was from 20°C to 100°C for heating and from 95°C to 25°C for cooling with step 10°C. A decrement in thermal diffusivity and thermal conductivity and an increment in specific heat values and electrical conductivity values with increasing temperature were observed. For the optimum nano-sheets concentration, an enhancement of the thermal conductivity by 35% was observed.

关键词： Conductivity Thermal conductivity Nanofluidics Temperature measurement Heating systems Temperature distribution Dielectrics

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Computation of Reference Trajectories for Inverted Pendulum with the Use of Two-point BvP with Free Parameters

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IFAC-PapersOnLine 2018年第6期51卷 408-413页

作者： Ozana, S. Schlegel, M. Department of Cybernetics and Biomedical Engineering Faculty of Electrical Engineering and Computer Science VSB-Technical University of Ostrava 17.listopadu 15 Ostrava Czech Republic Department of Cybernetics Faculty of Applied Sciences University of West Bohemia in Pilsen Technicka 8 Plzen Czech Republic

This paper deals with the computation of reference trajectories for inverted pendulum model to be used in two-degree of freedom control scheme. The proposed method uses a special customized candidate function for control signal u(t) involved in formulation of this case study as Two-point Boundary Value Problem with free parameters (TPBVP). © 2018

关键词： Trajectories Automation Boundary value problems Control engineering Control systems Degrees of freedom (mechanics) Pendulums State space methods Inverted pendulum model Reference trajectories State space models State trajectory TPBVP Trajectory Planning Two point boundary value problems Two degree of freedom control

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